This documentation is for development version 0.18.dev0.

mne.time_frequency.tfr_stockwell

mne.time_frequency.tfr_stockwell(inst, fmin=None, fmax=None, n_fft=None, width=1.0, decim=1, return_itc=False, n_jobs=1, verbose=None)[source]

Time-Frequency Representation (TFR) using Stockwell Transform.

Parameters:
inst : Epochs | Evoked

The epochs or evoked object.

fmin : None, float

The minimum frequency to include. If None defaults to the minimum fft frequency greater than zero.

fmax : None, float

The maximum frequency to include. If None defaults to the maximum fft.

n_fft : int | None

The length of the windows used for FFT. If None, it defaults to the next power of 2 larger than the signal length.

width : float

The width of the Gaussian window. If < 1, increased temporal resolution, if > 1, increased frequency resolution. Defaults to 1. (classical S-Transform).

decim : int

The decimation factor on the time axis. To reduce memory usage.

return_itc : bool

Return intertrial coherence (ITC) as well as averaged power.

n_jobs : int

The number of jobs to run in parallel (over channels).

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns:
power : AverageTFR

The averaged power.

itc : AverageTFR

The intertrial coherence. Only returned if return_itc is True.

Notes

New in version 0.9.0.